Search Results for author:
Found 18 papers, 9 papers with code
DREAM: A Dynamic Relational-Aware Model for Social Recommendation
In this paper, we propose a unified framework named Dynamic RElation Aware Model (DREAM) for social recommendation, which tries to model both users dynamic interests and their friends temporal influences.
Rethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter
In sharp contrast to the state-of-the-art (SOTA) methods that focus on learning pixel-wise saliency in "single image" using perceptual clues mainly, our method has investigated the "object-level semantic ranks between multiple images", of which the methodology is more consistent with the real human attention mechanism.
Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks
Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections.
A Deeper Look at Salient Object Detection: Bi-stream Network with a Small Training Dataset
Compared with the conventional hand-crafted approaches, the deep learning based methods have achieved tremendous performance improvements by training exquisitely crafted fancy networks over large-scale training sets.
UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks
The problem of session-based recommendation aims to predict user next actions based on session histories.
A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for Cold Start Users
Specifically, we first try to learn more effective user and item latent features in both source and target domains.
DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain
In this paper, we propose a Deep Cross Domain Insurance Recommendation System (DCDIR) for cold start users.
Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
Compared with static views, abundant dynamic properties between video frames are beneficial to refined depth estimation, especially for dynamic objects.
A neural document language modeling framework for spoken document retrieval
Recent developments in deep learning have led to a significant innovation in various classic and practical subjects, including speech recognition, computer vision, question answering, information retrieval and so on.
Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach
Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful.
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
We first discuss an innovative heuristic of cross-dataset training and evaluation, enabling the use of multiple single-task datasets (one with target task labels and the other with privacy labels) in our problem.
All-In-One Underwater Image Enhancement using Domain-Adversarial Learning
We train our model on a dataset consisting images of 10 Jerlov water types.
An Adaptive Oversampling Learning Method for Class-Imbalanced Fault Diagnostics and Prognostics
Data-driven fault diagnostics and prognostics suffers from class-imbalance problem in industrial systems and it raises challenges to common machine learning algorithms as it becomes difficult to learn the features of the minority class samples.
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study
This paper aims to improve privacy-preserving visual recognition, an increasingly demanded feature in smart camera applications, by formulating a unique adversarial training framework.
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
The current trend of pushing CNNs deeper with convolutions has created a pressing demand to achieve higher compression gains on CNNs where convolutions dominate the computation and parameter amount (e. g., GoogLeNet, ResNet and Wide ResNet).
SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection
To facilitate the task of expressing anomalies based on expert knowledge, our system provides a domain-specific query language, SAQL, which allows analysts to express models for (1) rule-based anomalies, (2) time-series anomalies, (3) invariant-based anomalies, and (4) outlier-based anomalies.
Cryptography and Security Databases
Deep $k$-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
The current trend of pushing CNNs deeper with convolutions has created a pressing demand to achieve higher compression gains on CNNs where convolutions dominate the computation and parameter amount (e. g., GoogLeNet, ResNet and Wide ResNet).
Behavior Query Discovery in System-Generated Temporal Graphs
In this work, we investigate how to query temporal graphs and treat query formulation as a discriminative temporal graph pattern mining problem.
